install.packages(c("rio", "tidyverse", "janitor", "lubridate", "rmarkdown", "fs", "hms", "zoo", "corrplot", "kableExtra"))Nomilo Fishpond Biogoechemical Analysis
Throughout this document, hover over the numbered annotations to the right of code chunks to reveal detailed explanations and comments about the code. Where drop-down italicized text is present, expand by pressing on arrow to see code.
Install Packages
Load Libraries
library(rio)
library(tidyverse)
library(janitor)
library(lubridate)
library(rmarkdown)
library(fs)
library(hms)
library(zoo)
library(corrplot)
library(kableExtra)- 1
- For importing excel data
- 2
- For cleaning of data
- 3
- For cleaning variable names
- 4
- For cleaning dates
- 5
- For displaying tables
- 6
- For file path usage
Import Raw Data
Procedure
Define vector of files to import:
files_to_import <- dir_ls(path = "data/raw")
for (i in seq_along(files_to_import)) {
cat(i, "= ", files_to_import[i], "\n")
}- 1
-
Store the file paths of our raw data within the
data/rawdirectory infiles_to_import - 2
- Print each file path with its index
1 = data/raw/2024-02-28_dfs.RData
2 = data/raw/2024-02-28_ksf-clam-growth.xlsx
3 = data/raw/2024-02-28_ksf-compiled-data.xlsx
4 = data/raw/2024-02-28_ksf-oyster-cylinder-growth.xlsx
5 = data/raw/2024-02-28_profile-data.xlsx
6 = data/raw/2024-02-28_water-samples.xlsx
7 = data/raw/2024-02-28_weather-data.xlsx
8 = data/raw/2024-03-01_dfs-no-profiles.RData
9 = data/raw/2024-03-01_dfs_no_profiles.RData
10 = data/raw/2024-03-04_dfs-no-profiles.RData
11 = data/raw/~$2024-02-28_weather-data.xlsx
Use the purrr::map() function to iteratively import files in the files_to_import vector except for the profiles data and .RData files:
The @iteratively-import-raw-data code chunk should only be ran once when raw data is updated because it takes long to execute. Therefore, run the @efficiently-load-raw-data code chunk instead to easily import up-to-date raw data.
dfs_no_profiles <- map(files_to_import[c(2:4, 6, 7)], import_list)
current_date <- format(Sys.Date(), "%Y-%m-%d")
save(dfs_no_profiles, file = paste0("data/raw/", current_date, "_dfs-no-profiles.RData"))Refer to the output of the files_to_import data object to ensure you are inputting the correct index value corresponding to the file path that needs to be loaded.
Efficiently import up-to-date raw data:
load(files_to_import[10])Rename datasets:
We will always use snakecase when naming our data objects and functions (e.g., data_object_name or function_name()).
names(dfs_no_profiles) <- gsub("data/raw/2024-02-28_|\\.xlsx$|\\.xls$", "",
files_to_import[c(2:4, 6, 7)])
names(dfs_no_profiles) <- gsub("-", "_", names(dfs_no_profiles))
names(dfs_no_profiles)- 1
- Remove prefixes and file extensions
- 2
- Replace hyphens with underscores
- 3
- Check if names were outputted correctly
[1] "ksf_clam_growth" "ksf_compiled_data"
[3] "ksf_oyster_cylinder_growth" "water_samples"
[5] "weather_data"
Rename each sheet within each raw dataset to be lowercased and replace spaces with underscores:
dfs_no_profiles <- map(dfs_no_profiles, ~ set_names(.x, gsub(" ", "_", tolower(names(.x)))))Create separate datasets by specifying the Excel sheet from each spreadsheet we want to tidy:
ksf_clams_growth_data <- dfs_no_profiles$ksf_clam_growth$sheet1
ksf_compiled_data <- dfs_no_profiles$ksf_compiled_data$full_data
ksf_oyster_cylinder_growth_data <- dfs_no_profiles$ksf_oyster_cylinder_growth$sheet1
water_samples_data <- dfs_no_profiles$water_samples$data_overview
weather_data <- dfs_no_profiles$weather_data$weather_ksf
tidal_data <- dfs_no_profiles$ksf_compiled_data$tidesWe want to combine multiple sheets within the profiles Excel spreadsheet into one, therefore, we will import it separately:
sheets_to_import <- c("L1", "L2", "L3", "L4")
profiles_data <- profiles_data <- map_dfr(sheets_to_import, function(sheet_name) {
import(files_to_import[5], which = sheet_name)
}) %>%
bind_rows()- 1
- [code annotation]
- 2
- [code annotation]
- 3
- [code annotation]
View Raw Data
Tidy Raw Data
Tidying Processes
Steps to clean data
new_clam_var_names <- c(
"sort_date", "color", "clams_in_count", "clams_in_lbs", "clams_in_avg_per_lb",
"clams_out_count", "clams_out_lbs", "clams_out_avg_per_lb", "growth_in_lbs",
"growth_pct", "sr", "days_btwn_sort"
)
new_clam_date_col <- c(
"2023-10-17", "2023-12-06", "2023-12-12", "2024-01-02", "2024-01-10", "2024-01-24",
"2024-01-31", "2024-02-08", "2024-02-13"
)
ksf_clams_growth_data_tidied <- ksf_clams_growth_data %>%
slice(-1) %>%
setNames(new_clam_var_names) %>%
mutate(date = as.Date(new_clam_date_col)) %>%
dplyr::select(-sort_date) %>%
pivot_longer(
cols = c(
clams_in_count, clams_in_lbs, clams_in_avg_per_lb, clams_out_count,
clams_out_lbs, clams_out_avg_per_lb
),
names_to = c("stage", ".value"),
names_prefix = "clams_",
names_sep = "_",
values_to = "value"
) %>%
mutate(stage = if_else(str_detect(stage, "in"), "In", "Out")) %>%
rename(avg_per_lbs = avg) %>%
mutate(across(c(color, stage), as.factor)) %>%
mutate(across(c(count, lbs, avg_per_lbs, growth_in_lbs, growth_pct, sr),
~as.numeric(gsub("%", "", .)))) %>%
arrange(date, color, stage) %>%
dplyr::select(date, days_btwn_sort, color, stage, count, lbs, avg_per_lbs,
growth_in_lbs, growth_pct, sr) %>%
rename("days_btwn_clams_sort" = days_btwn_sort,
"clams_color" = color,
"clams_stage" = stage,
"clams_count" = count,
"weight" = lbs,
"avg_weight" = avg_per_lbs,
"clams_growth" = growth_in_lbs,
"clams_sr" = sr)
paged_table(ksf_clams_growth_data_tidied)- 1
- Manually set variable names
- 2
- Assign dates to new date column
- 3
- Delete first row
- 4
- Set date as correct variable type and pivot data set based on date range.
- 5
- Assign In and Out to stage
- 6
- Rename variable of average to average per lbs
- 7
- Set stage and color as factor variable types
- 8
- Set variables as numeric variable types
- 9
- Arrange values by date, color, and stage
Steps to clean data
ksf_compiled_data_tidied <- ksf_compiled_data %>%
rename_with(~gsub("\\s*\\([^\\)]+\\)", "", .x)) %>%
janitor::clean_names() %>%
rename(date = date_time) %>%
mutate(date = as.Date(date)) %>%
filter(date >= as.Date("2023-11-20") & date <= as.Date("2024-02-20")) %>%
arrange(date) %>%
dplyr::select(-c(external_voltage, wk_num, wind_dir,
spadd, outdoor_temperature, hourly_rain,
solar_radiation, resistivity, battery_capacity,
hour, daynum, data_pt, wind_sp, diradd,
wind_speed, wind_direction, tide, day, month, year)
) %>%
dplyr::select(where(~ !anyNA(.))) %>%
group_by(date) %>%
summarise(across(where(is.numeric), \(x) mean(x, na.rm = TRUE))) %>%
rename("ksf_salinity" = salinity,
"ksf_rdo_saturation" = rdo_saturation,
"ksf_rdo_concentration" = rdo_concentration,
"ksf_actual_conductivity" = actual_conductivity,
"ksf_total_dissolved_solids" = total_dissolved_solids,
"ksf_ammonium" = ammonium,
"ksf_barometric_pressure" = barometric_pressure,
"ksf_oxygen_partial_pressure" = oxygen_partial_pressure,
"ksf_specific_conductivity" = specific_conductivity,
"ksf_density" = density,
"ksf_chlorophyll_a_fluorescence" = chlorophyll_a_fluorescence,
"ksf_ammonium_m_v" = ammonium_m_v)
paged_table(ksf_compiled_data_tidied)- 1
- Clean variable names by removing everything in parentheses, using lowercase and underscores in place of spaces
- 2
-
Rename the
date_timevariable todate, filter to desired date range and sort bydate - 3
- Remove unnecessary variables
- 4
- Remove columns with containing all NA values
- 5
-
Group by
dateand calculate the average of every variable for each day
Steps to clean data
oyster_var_names <- c(
"date", "oyster_large_weight", "oyster_large_gain", "oyster_small_weight",
"oyster_small_gain", "oyster_chlorophyll"
)
ksf_oyster_cylinder_growth_data_tidied <- ksf_oyster_cylinder_growth_data %>%
dplyr::select(c(1, 4, 5, 8, 9, 12)) %>%
slice(-1) %>%
setNames(oyster_var_names) %>%
pivot_longer(
cols = c(oyster_large_weight, oyster_large_gain,
oyster_small_gain,
oyster_small_weight),
names_to = c("oyster_size", ".value"),
names_prefix = "oyster_",
names_sep = "_",
values_to = "value"
) %>%
mutate(oyster_size = if_else(str_detect(oyster_size, "small"), "Small", "Large")) %>%
mutate(date = as.Date(date),
oyster_size = as.factor(oyster_size),
across(c(weight, gain), as.numeric)
) %>%
filter(date >= as.Date("2023-11-20") & date <=
as.Date("2024-02-14")) %>%
mutate(weight = weight * 0.00220462) %>%
rename("growth_pct" = gain)
paged_table(ksf_oyster_cylinder_growth_data_tidied)- 1
- Manually set variable names
- 2
- Select desired columns and remove first row
- 3
- Convert from wide to long format
- 4
- Create a new variable that differentiates oyster size
- 5
- Adjust data types to numeric and factor
- 6
- Filter to desired date range
Steps to clean data
water_samples_data_tidied <- water_samples_data %>%
slice(-c(44:52)) %>%
rename_with(~gsub("\\s*\\([^\\)]+\\)", "", .x)) %>%
janitor::clean_names() %>%
mutate(
date = if_else(date == "44074",
as.character(as.Date("2024-01-09")),
format(dmy(date), "%Y-%m-%d"))
) %>%
mutate(sample_id = 1:nrow(.)) %>%
mutate(date = as.Date(date),
across(c(nomilo_id, location, round, depth), as.factor)) %>%
select(-c(sample_id, nomilo_id, tube_name))
paged_table(water_samples_data_tidied)- 1
- Clean variable names by removing everything in parentheses, using lowercase and underscores in place of spaces
- 2
- Replaces incorrect date values and format as YYYY-MM-DD
- 3
- Add values for sample ID
- 4
- Set correct variable types
Steps to clean data
weather_data_tidied <- weather_data %>%
janitor::clean_names() %>%
unite(date, year, month, day, sep = "-") %>%
mutate(date = ymd(date)) %>%
select(-c(1, 3)) %>%
rename("outdoor_temperature" = outdoor_temp_f) %>%
mutate(outdoor_temperature = (outdoor_temperature - 32) * (5/9)) %>%
group_by(date) %>%
summarise(across(where(is.numeric), \(x) mean(x, na.rm = TRUE))) %>%
slice(-1)
paged_table(weather_data_tidied)- 1
- Clean variable names
- 2
- Merge separate day, month, year columns into one column variable and format as YYYY-MM-DD.
- 3
- Cut columns
- 4
- Rename outdoor temperature and convert from Fahrenheit to Celcius
- 5
- Group by date and then take average values per day
- 6
- Cut first row
Steps to clean data
new_profile_var_names <- c("depth", "water_temperature", "dissolved_oxygen", "salinity", "conductivity", "visibility", "location", "date")
profiles_data_tidied <- profiles_data %>%
select(-c(6, 8)) %>%
mutate(
temp_column1 = NA_character_,
temp_column2 = NA_character_
) %>%
setNames(new_profile_var_names) %>%
mutate(
location = ifelse(depth == "Location", water_temperature, NA_character_),
date = ifelse(depth == "Date", water_temperature, NA_character_)
) %>%
fill(location, date, .direction = "down") %>%
filter(depth != "Location", depth != "Date") %>%
mutate(
location = case_when(
location == "L1 Northwest buoy" ~ "back buoy",
location == "L2 Middle Buoy" ~ "mid buoy",
location == "L3 Production Dock" ~ "production dock",
location == "L4 Auwai" ~ "auwei",
TRUE ~ location
),
date = case_when(
date %in% c("45258", "2023-11-28") ~ "2023-11-28",
date %in% c("45282", "2023-12-21") ~ "2023-12-21",
date %in% c("45536", "2024-01-09") ~ "2024-01-09",
date %in% c("30/1/24", "30/01/24") ~ "2024-01-30",
date %in% c("20/02/24", "20/2/24") ~ "2024-02-20",
TRUE ~ date
)) %>%
mutate(
date = as.Date(date, format = "%Y-%m-%d"),
conductivity = case_when(
row_number() %in% c(1:11) ~ NA_character_,
TRUE ~ as.character(conductivity)
)
) %>%
filter(!(depth %in% c("Samples", "Depth"))) %>%
mutate(date = as.Date(date),
across(c(depth, location), as.factor),
across(c(water_temperature, dissolved_oxygen, salinity,
conductivity,visibility), as.numeric)) %>%
fill(visibility, .direction = "down") %>%
mutate(visibility = if_else(date == "2023-11-28", NA_real_, visibility))
paged_table(profiles_data_tidied)- 1
- Set new variable names manually
- 2
- Delete unnecessary columns
- 3
- Temporarily create two new columns to replace the ones we deleted
- 4
- Assign new profile variable names to rename variables in data set
- 5
- Takes location from one column of location and date data, and assigns it to corresponding data of another column.
- 6
- Fill values of temperature downwards in newly created date and location column.
- 7
- Gets rid of the ‘location’ and ‘date’ rows that had empty values.
- 8
- Renames values
- 9
- Removes turbidity data rows 1:11
- 10
- Looks for rows containing samples and depth names and negate these values.
- 11
- Sets correct data types for each variable
- 12
- Fills values from the temperature downwards into the newly created columns for date and location
Steps to clean data
tidal_data_tidied <- tidal_data %>%
janitor::clean_names() %>%
mutate(date = as.Date(date, format = "%Y-%m-%d")) %>%
filter(date >= as.Date("2023-11-20") & date <= as.Date("2024-02-20")) %>%
select(-2) %>%
mutate(time = as_hms(format(time, "%H:%M:%S")),
high_low = as.factor(high_low))
paged_table(tidal_data_tidied)- 1
- Clean variable names
- 2
- Set date as correct variable type and format YYYY-MM-DD
- 3
- Filter to desired date range
- 4
- Cut column
- 5
- Set time as time variable type
- 6
- Set variable as factor type
Merge and Interpolate Tidied Datasets
Clams Growth Merged with Environmental Variables
clams_growth_env_vars_merged <- left_join(env_vars, ksf_clams_growth_data_tidied, by = c("closest_date" = "date"))
paged_table(clams_growth_env_vars_merged)Oyster Growth Interpolated and Merged with Environmental Variables
oyster_growth_env_vars_merged <- ksf_oyster_cylinder_growth_data_tidied %>%
mutate(closest_date = case_when(
date == "2023-12-11" ~ as.Date("2023-12-12"),
date == "2024-01-08" ~ as.Date("2024-01-10"),
date == "2024-01-23" ~ as.Date("2024-01-31"),
date == "2024-02-14" ~ as.Date("2024-02-13")
)) %>%
mutate(across(any_of(continuous_vars), ~na.fill(na.approx(.x, na.rm = FALSE), "extend"), .names = "interp_{.col}")) %>%
select(-c(date, oyster_chlorophyll, weight, growth_pct)) %>%
filter(!is.na(closest_date)) %>%
right_join(., env_vars, by = "closest_date") %>%
relocate(closest_date, round, location, depth, .before = oyster_size) %>%
arrange(closest_date)
paged_table(oyster_growth_env_vars_merged)Export Tidied Datasets
Export tidied datasets to CSV into data/tidied folder:
source("code/functions/export_to_csv.R")
dfs_to_export <- list(
ksf_clams_growth_data_tidied = ksf_clams_growth_data_tidied,
ksf_compiled_data_tidied = ksf_compiled_data_tidied,
ksf_oyster_cylinder_growth_data_tidied = ksf_oyster_cylinder_growth_data_tidied,
water_samples_data_tidied = water_samples_data_tidied,
profiles_data_tidied = profiles_data_tidied
)
imap(dfs_to_export, ~ export_to_csv(.x, .y, "data/tidied"))- 1
- List of dataframes we want to export as CSV files
- 2
-
Iterate the
export_to_csv(df, df_name, dir_path)function over each dataframe..xrefers to the dataframe..yrefers to the name of the dataframe. These are passed toexport_to_csv()function along with the desired directory path.
Export merged final data set into data/outputs folder.
Data Dictionary
Correlational Analysis
| interp_water_temperature | interp_dissolved_oxygen | interp_conductivity | interp_visibility | interp_chlorophyll_a | interp_phosphate | interp_silicate | interp_nitrate_nitrite | interp_ammonia | interp_heterotrophic_bacteria | interp_large_phytoplankton | interp_synechococcus_population_1 | interp_synechococcus_population_2 | interp_prochlorococcus | interp_lysbeths_mystery_cells_events | ksf_rdo_concentration | ksf_rdo_saturation | ksf_oxygen_partial_pressure | ksf_actual_conductivity | ksf_specific_conductivity | ksf_salinity | ksf_density | ksf_total_dissolved_solids | ksf_chlorophyll_a_fluorescence | ksf_ammonium | ksf_ammonium_m_v | ksf_barometric_pressure | outdoor_temperature | wind_speed_mph | hourly_rain_inch_hr | wind_direction | days_btwn_clams_sort | clams_count | weight | avg_weight | clams_growth | growth_pct | clams_sr | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| interp_water_temperature | 1.0000000 | -0.0657204 | -0.2650281 | 0.6482057 | 0.1101017 | -0.3109539 | -0.4805062 | 0.1818541 | 0.2940774 | -0.0599312 | -0.5934387 | -0.2441516 | -0.1728060 | -0.3813279 | -0.4479796 | 0.2397450 | 0.1168555 | 0.1249848 | -0.5234111 | -0.4996062 | -0.5019132 | -0.4737839 | -0.4996059 | -0.4232381 | -0.2669770 | -0.1954463 | 0.1456517 | -0.5416504 | -0.1565951 | -0.5853932 | 0.4160472 | 0.6221002 | 0.6513410 | -0.1435614 | 0.5857689 | -0.2647005 | -0.3005932 | 0.8831186 |
| interp_dissolved_oxygen | -0.0657204 | 1.0000000 | 0.2319534 | -0.2129630 | -0.2818786 | -0.1636619 | -0.1108632 | -0.2637840 | -0.3180597 | -0.2899848 | 0.2351851 | -0.1355177 | -0.2045156 | -0.2169682 | 0.2781064 | 0.1148541 | 0.1173851 | 0.1177972 | 0.1023190 | 0.1336775 | 0.1322711 | 0.1591253 | 0.1336775 | 0.3167944 | -0.2036098 | -0.2235560 | -0.1488079 | 0.1616038 | -0.0418597 | -0.0704403 | 0.1274341 | -0.1820372 | -0.1095267 | -0.0272633 | -0.1115570 | -0.0922296 | -0.1028283 | -0.1677426 |
| interp_conductivity | -0.2650281 | 0.2319534 | 1.0000000 | -0.3550157 | -0.5998299 | -0.0795675 | -0.1452676 | 0.1190473 | -0.2152160 | -0.2226307 | 0.3888135 | -0.2263441 | -0.2632717 | -0.4589120 | 0.4920133 | 0.1291550 | 0.1098263 | 0.1120566 | 0.0157606 | 0.0680932 | 0.0656345 | 0.1127614 | 0.0680932 | 0.4853660 | -0.3696082 | -0.3920589 | -0.1268462 | 0.1132979 | 0.0184732 | -0.2176432 | 0.2995844 | -0.3004961 | -0.1723985 | -0.1060128 | -0.1592718 | -0.2645419 | -0.2785850 | -0.1364766 |
| interp_visibility | 0.6482057 | -0.2129630 | -0.3550157 | 1.0000000 | 0.5522508 | -0.0947801 | -0.2502225 | 0.2410925 | 0.4725783 | 0.0768136 | -0.5494701 | -0.1840184 | -0.0919120 | -0.1334711 | -0.5326582 | 0.2514661 | 0.1940241 | 0.1973939 | -0.1993019 | -0.2037581 | -0.2043899 | -0.2065081 | -0.2037579 | -0.5262559 | -0.0038245 | 0.0506186 | -0.0182157 | -0.2594965 | -0.2937780 | -0.2338873 | 0.0701091 | 0.6775773 | 0.6295397 | 0.0286361 | 0.5385243 | 0.0843004 | 0.0537708 | 0.5883696 |
| interp_chlorophyll_a | 0.1101017 | -0.2818786 | -0.5998299 | 0.5522508 | 1.0000000 | 0.2856368 | 0.3588692 | 0.0276973 | 0.2930864 | 0.4432634 | -0.4542994 | 0.3410615 | 0.3920397 | 0.4771970 | -0.5799557 | -0.2051033 | -0.1610904 | -0.1652734 | 0.0578687 | -0.0154719 | -0.0118535 | -0.0789170 | -0.0154719 | -0.5871394 | 0.5329029 | 0.5524064 | 0.1454145 | -0.0698531 | -0.0047797 | 0.3771542 | -0.4621504 | 0.3127845 | 0.1372108 | 0.1638352 | 0.1285667 | 0.3943756 | 0.4181650 | 0.0524740 |
| interp_phosphate | -0.3109539 | -0.1636619 | -0.0795675 | -0.0947801 | 0.2856368 | 1.0000000 | 0.5660911 | 0.1390703 | 0.4437642 | 0.2403967 | 0.0704494 | 0.1868674 | 0.2298244 | 0.3269728 | -0.0371624 | -0.2008496 | -0.1360496 | -0.1407781 | 0.2145857 | 0.1783425 | 0.1806208 | 0.1452450 | 0.1783423 | -0.0191587 | 0.2977059 | 0.2746158 | 0.0199737 | 0.1786904 | 0.0981261 | 0.3699987 | -0.3248341 | -0.1796265 | -0.2513523 | 0.1041795 | -0.2188578 | 0.2241136 | 0.2500432 | -0.3468351 |
| interp_silicate | -0.4805062 | -0.1108632 | -0.1452676 | -0.2502225 | 0.3588692 | 0.5660911 | 1.0000000 | 0.1222968 | -0.0004807 | 0.7401299 | -0.0510525 | 0.7769636 | 0.7358405 | 0.6781906 | -0.1454307 | -0.8116844 | -0.7465947 | -0.7519289 | -0.1194767 | -0.2052439 | -0.2001345 | -0.2742370 | -0.2052442 | -0.1160208 | 0.5831631 | 0.5629555 | 0.5838129 | -0.1978858 | 0.6883297 | 0.5008472 | -0.4131914 | -0.5694877 | -0.6992874 | -0.0219724 | -0.5285234 | 0.0331869 | 0.1181575 | -0.3359967 |
| interp_nitrate_nitrite | 0.1818541 | -0.2637840 | 0.1190473 | 0.2410925 | 0.0276973 | 0.1390703 | 0.1222968 | 1.0000000 | 0.3335486 | 0.4218813 | -0.2415770 | -0.0082736 | 0.1348142 | -0.0378669 | -0.2408958 | -0.1928328 | -0.2491312 | -0.2456062 | -0.4010053 | -0.4102686 | -0.4101014 | -0.4124758 | -0.4102686 | -0.1854731 | -0.0112523 | 0.0195065 | 0.3396210 | -0.4204995 | 0.2347295 | -0.2152257 | 0.1706990 | 0.0439745 | 0.0407731 | -0.1393557 | 0.0839105 | -0.2429381 | -0.2300955 | 0.3819963 |
| interp_ammonia | 0.2940774 | -0.3180597 | -0.2152160 | 0.4725783 | 0.2930864 | 0.4437642 | -0.0004807 | 0.3335486 | 1.0000000 | -0.0381747 | -0.2510432 | -0.3243820 | -0.1985097 | -0.0641393 | -0.2892883 | 0.2963946 | 0.2800056 | 0.2809420 | 0.0532133 | 0.0586622 | 0.0579085 | 0.0612364 | 0.0586623 | -0.2640421 | -0.0264327 | -0.0027462 | -0.1946828 | 0.0254673 | -0.3376340 | -0.0847654 | -0.0095141 | 0.4636865 | 0.4392980 | 0.0833024 | 0.3501727 | 0.1647727 | 0.1342378 | 0.2394138 |
| interp_heterotrophic_bacteria | -0.0599312 | -0.2899848 | -0.2226307 | 0.0768136 | 0.4432634 | 0.2403967 | 0.7401299 | 0.4218813 | -0.0381747 | 1.0000000 | -0.5360501 | 0.8789411 | 0.9205836 | 0.6812707 | -0.5629514 | -0.8877300 | -0.8678669 | -0.8707803 | -0.4252525 | -0.5352751 | -0.5294539 | -0.6212698 | -0.5352753 | -0.5551741 | 0.6979044 | 0.7207483 | 0.8204889 | -0.5668166 | 0.7265675 | 0.3710543 | -0.3893556 | -0.2266657 | -0.4162456 | -0.0598711 | -0.2535510 | 0.0045864 | 0.0920091 | 0.1233148 |
| interp_large_phytoplankton | -0.5934387 | 0.2351851 | 0.3888135 | -0.5494701 | -0.4542994 | 0.0704494 | -0.0510525 | -0.2415770 | -0.2510432 | -0.5360501 | 1.0000000 | -0.3648509 | -0.5254498 | -0.3812589 | 0.9552025 | 0.2603205 | 0.2744921 | 0.2751979 | 0.2942632 | 0.3800012 | 0.3762880 | 0.4496783 | 0.3800012 | 0.9449553 | -0.5543873 | -0.6166687 | -0.3861873 | 0.4652032 | -0.0522510 | -0.1615462 | 0.3429615 | -0.6107256 | -0.4051013 | -0.0857350 | -0.3928876 | -0.2771411 | -0.3001309 | -0.5405267 |
| interp_synechococcus_population_1 | -0.2441516 | -0.1355177 | -0.2263441 | -0.1840184 | 0.3410615 | 0.1868674 | 0.7769636 | -0.0082736 | -0.3243820 | 0.8789411 | -0.3648509 | 1.0000000 | 0.9436791 | 0.7480325 | -0.3837403 | -0.9419402 | -0.8939279 | -0.8986056 | -0.2995903 | -0.4103768 | -0.4042186 | -0.4982813 | -0.4103771 | -0.3976795 | 0.7266781 | 0.7293942 | 0.7791376 | -0.4263505 | 0.7765453 | 0.4930527 | -0.4596896 | -0.4200687 | -0.6033655 | -0.0383574 | -0.4208461 | 0.0371382 | 0.1327635 | -0.1097456 |
| interp_synechococcus_population_2 | -0.1728060 | -0.2045156 | -0.2632717 | -0.0919120 | 0.3920397 | 0.2298244 | 0.7358405 | 0.1348142 | -0.1985097 | 0.9205836 | -0.5254498 | 0.9436791 | 1.0000000 | 0.8293406 | -0.5742000 | -0.8816949 | -0.8200803 | -0.8259709 | -0.2159316 | -0.3412148 | -0.3344355 | -0.4430526 | -0.3412150 | -0.5834799 | 0.8464878 | 0.8561089 | 0.7127158 | -0.3777729 | 0.6496602 | 0.5881599 | -0.5947605 | -0.2241806 | -0.4551523 | 0.0425072 | -0.3052701 | 0.2091450 | 0.3001589 | -0.0838449 |
| interp_prochlorococcus | -0.3813279 | -0.2169682 | -0.4589120 | -0.1334711 | 0.4771970 | 0.3269728 | 0.6781906 | -0.0378669 | -0.0641393 | 0.6812707 | -0.3812589 | 0.7480325 | 0.8293406 | 1.0000000 | -0.5429470 | -0.6404636 | -0.5195533 | -0.5293928 | 0.2122566 | 0.0868102 | 0.0937653 | -0.0215978 | 0.0868100 | -0.5517825 | 0.9305750 | 0.9181134 | 0.3403672 | 0.0438956 | 0.3240315 | 0.8403012 | -0.8389353 | -0.1019712 | -0.3622227 | 0.2295376 | -0.2803506 | 0.5564891 | 0.6301548 | -0.4005846 |
| interp_lysbeths_mystery_cells_events | -0.4479796 | 0.2781064 | 0.4920133 | -0.5326582 | -0.5799557 | -0.0371624 | -0.1454307 | -0.2408958 | -0.2892883 | -0.5629514 | 0.9552025 | -0.3837403 | -0.5742000 | -0.5429470 | 1.0000000 | 0.2609594 | 0.2396224 | 0.2428721 | 0.1197646 | 0.2192529 | 0.2146858 | 0.3030029 | 0.2192529 | 0.9739163 | -0.6855490 | -0.7368587 | -0.3003455 | 0.3090457 | 0.0085482 | -0.3481968 | 0.5201100 | -0.6125326 | -0.3703103 | -0.1723642 | -0.3481807 | -0.4511572 | -0.4777556 | -0.3664809 |
| ksf_rdo_concentration | 0.2397450 | 0.1148541 | 0.1291550 | 0.2514661 | -0.2051033 | -0.2008496 | -0.8116844 | -0.1928328 | 0.2963946 | -0.8877300 | 0.2603205 | -0.9419402 | -0.8816949 | -0.6404636 | 0.2609594 | 1.0000000 | 0.9841108 | 0.9863184 | 0.4772012 | 0.5695713 | 0.5643500 | 0.6390054 | 0.5695716 | 0.2408733 | -0.5611369 | -0.5654086 | -0.8940100 | 0.5669547 | -0.9224867 | -0.3089709 | 0.2502215 | 0.5779943 | 0.7038381 | 0.1555622 | 0.4875758 | 0.2042601 | 0.1061208 | 0.0223096 |
| ksf_rdo_saturation | 0.1168555 | 0.1173851 | 0.1098263 | 0.1940241 | -0.1610904 | -0.1360496 | -0.7465947 | -0.2491312 | 0.2800056 | -0.8678669 | 0.2744921 | -0.8939279 | -0.8200803 | -0.5195533 | 0.2396224 | 0.9841108 | 1.0000000 | 0.9999156 | 0.6249132 | 0.7037013 | 0.6993164 | 0.7596117 | 0.7037015 | 0.2120478 | -0.4363655 | -0.4503518 | -0.9566189 | 0.6971053 | -0.9622048 | -0.1385889 | 0.0893036 | 0.5702458 | 0.6617840 | 0.2300022 | 0.4400770 | 0.3553993 | 0.2619690 | -0.1328993 |
| ksf_oxygen_partial_pressure | 0.1249848 | 0.1177972 | 0.1120566 | 0.1973939 | -0.1652734 | -0.1407781 | -0.7519289 | -0.2456062 | 0.2809420 | -0.8707803 | 0.2751979 | -0.8986056 | -0.8259709 | -0.5293928 | 0.2428721 | 0.9863184 | 0.9999156 | 1.0000000 | 0.6149809 | 0.6949539 | 0.6904989 | 0.7520040 | 0.6949541 | 0.2158518 | -0.4466848 | -0.4600720 | -0.9532733 | 0.6887858 | -0.9599856 | -0.1514105 | 0.1017018 | 0.5701592 | 0.6646049 | 0.2245568 | 0.4431781 | 0.3441033 | 0.2502320 | -0.1225183 |
| ksf_actual_conductivity | -0.5234111 | 0.1023190 | 0.0157606 | -0.1993019 | 0.0578687 | 0.2145857 | -0.1194767 | -0.4010053 | 0.0532133 | -0.4252525 | 0.2942632 | -0.2995903 | -0.2159316 | 0.2122566 | 0.1197646 | 0.4772012 | 0.6249132 | 0.6149809 | 1.0000000 | 0.9906982 | 0.9916629 | 0.9680363 | 0.9906981 | 0.0739218 | 0.2593693 | 0.2040619 | -0.8149692 | 0.9777205 | -0.6879652 | 0.6514362 | -0.6198918 | 0.2131128 | 0.1315758 | 0.4374463 | -0.0086257 | 0.8195035 | 0.7882526 | -0.7939462 |
| ksf_specific_conductivity | -0.4996062 | 0.1336775 | 0.0680932 | -0.2037581 | -0.0154719 | 0.1783425 | -0.2052439 | -0.4102686 | 0.0586622 | -0.5352751 | 0.3800012 | -0.4103768 | -0.3412148 | 0.0868102 | 0.2192529 | 0.5695713 | 0.7037013 | 0.6949539 | 0.9906982 | 1.0000000 | 0.9999729 | 0.9931595 | 1.0000000 | 0.1764503 | 0.1255371 | 0.0692212 | -0.8766105 | 0.9952781 | -0.7349847 | 0.5453456 | -0.5073939 | 0.2058966 | 0.1634997 | 0.4087931 | 0.0125403 | 0.7459491 | 0.7044802 | -0.7683491 |
| ksf_salinity | -0.5019132 | 0.1322711 | 0.0656345 | -0.2043899 | -0.0118535 | 0.1806208 | -0.2001345 | -0.4101014 | 0.0579085 | -0.5294539 | 0.3762880 | -0.4042186 | -0.3344355 | 0.0937653 | 0.2146858 | 0.5643500 | 0.6993164 | 0.6904989 | 0.9916629 | 0.9999729 | 1.0000000 | 0.9922847 | 0.9999729 | 0.1717245 | 0.1327587 | 0.0764089 | -0.8734302 | 0.9948862 | -0.7321703 | 0.5514814 | -0.5136329 | 0.2053523 | 0.1609126 | 0.4103663 | 0.0106343 | 0.7499172 | 0.7090337 | -0.7707062 |
| ksf_density | -0.4737839 | 0.1591253 | 0.1127614 | -0.2065081 | -0.0789170 | 0.1452450 | -0.2742370 | -0.4124758 | 0.0612364 | -0.6212698 | 0.4496783 | -0.4982813 | -0.4430526 | -0.0215978 | 0.3030029 | 0.6390054 | 0.7596117 | 0.7520040 | 0.9680363 | 0.9931595 | 0.9922847 | 1.0000000 | 0.9931596 | 0.2635036 | 0.0088565 | -0.0476442 | -0.9157638 | 0.9957898 | -0.7628128 | 0.4467497 | -0.4033057 | 0.1940711 | 0.1860633 | 0.3777678 | 0.0285551 | 0.6709850 | 0.6215102 | -0.7365729 |
| ksf_total_dissolved_solids | -0.4996059 | 0.1336775 | 0.0680932 | -0.2037579 | -0.0154719 | 0.1783423 | -0.2052442 | -0.4102686 | 0.0586623 | -0.5352753 | 0.3800012 | -0.4103771 | -0.3412150 | 0.0868100 | 0.2192529 | 0.5695716 | 0.7037015 | 0.6949541 | 0.9906981 | 1.0000000 | 0.9999729 | 0.9931596 | 1.0000000 | 0.1764503 | 0.1255369 | 0.0692210 | -0.8766106 | 0.9952781 | -0.7349849 | 0.5453453 | -0.5073937 | 0.2058969 | 0.1634999 | 0.4087931 | 0.0125405 | 0.7459490 | 0.7044801 | -0.7683489 |
| ksf_chlorophyll_a_fluorescence | -0.4232381 | 0.3167944 | 0.4853660 | -0.5262559 | -0.5871394 | -0.0191587 | -0.1160208 | -0.1854731 | -0.2640421 | -0.5551741 | 0.9449553 | -0.3976795 | -0.5834799 | -0.5517825 | 0.9739163 | 0.2408733 | 0.2120478 | 0.2158518 | 0.0739218 | 0.1764503 | 0.1717245 | 0.2635036 | 0.1764503 | 1.0000000 | -0.7160863 | -0.7664177 | -0.2663666 | 0.2697243 | 0.0465964 | -0.3878585 | 0.5641272 | -0.6455127 | -0.3925326 | -0.1992495 | -0.3618281 | -0.5050765 | -0.5304635 | -0.3411648 |
| ksf_ammonium | -0.2669770 | -0.2036098 | -0.3696082 | -0.0038245 | 0.5329029 | 0.2977059 | 0.5831631 | -0.0112523 | -0.0264327 | 0.6979044 | -0.5543873 | 0.7266781 | 0.8464878 | 0.9305750 | -0.6855490 | -0.5611369 | -0.4363655 | -0.4466848 | 0.2593693 | 0.1255371 | 0.1327587 | 0.0088565 | 0.1255369 | -0.7160863 | 1.0000000 | 0.9967078 | 0.2787391 | 0.0641183 | 0.1981271 | 0.8781148 | -0.9183738 | 0.0965142 | -0.1973273 | 0.2885511 | -0.1487867 | 0.6817656 | 0.7480121 | -0.3326922 |
| ksf_ammonium_m_v | -0.1954463 | -0.2235560 | -0.3920589 | 0.0506186 | 0.5524064 | 0.2746158 | 0.5629555 | 0.0195065 | -0.0027462 | 0.7207483 | -0.6166687 | 0.7293942 | 0.8561089 | 0.9181134 | -0.7368587 | -0.5654086 | -0.4503518 | -0.4600720 | 0.2040619 | 0.0692212 | 0.0764089 | -0.0476442 | 0.0692210 | -0.7664177 | 0.9967078 | 1.0000000 | 0.3133194 | 0.0034481 | 0.2049762 | 0.8393636 | -0.8942197 | 0.1420857 | -0.1544615 | 0.2748945 | -0.1069287 | 0.6597090 | 0.7253510 | -0.2556958 |
| ksf_barometric_pressure | 0.1456517 | -0.1488079 | -0.1268462 | -0.0182157 | 0.1454145 | 0.0199737 | 0.5838129 | 0.3396210 | -0.1946828 | 0.8204889 | -0.3861873 | 0.7791376 | 0.7127158 | 0.3403672 | -0.3003455 | -0.8940100 | -0.9566189 | -0.9532733 | -0.8149692 | -0.8766105 | -0.8734302 | -0.9157638 | -0.8766106 | -0.2663666 | 0.2787391 | 0.3133194 | 1.0000000 | -0.8754851 | 0.9298068 | -0.1048372 | 0.1126403 | -0.4251163 | -0.4828256 | -0.3051411 | -0.2770414 | -0.5063542 | -0.4263817 | 0.4143299 |
| outdoor_temperature | -0.5416504 | 0.1616038 | 0.1132979 | -0.2594965 | -0.0698531 | 0.1786904 | -0.1978858 | -0.4204995 | 0.0254673 | -0.5668166 | 0.4652032 | -0.4263505 | -0.3777729 | 0.0438956 | 0.3090457 | 0.5669547 | 0.6971053 | 0.6887858 | 0.9777205 | 0.9952781 | 0.9948862 | 0.9957898 | 0.9952781 | 0.2697243 | 0.0641183 | 0.0034481 | -0.8754851 | 1.0000000 | -0.7034344 | 0.5071226 | -0.4507075 | 0.1271159 | 0.1078947 | 0.3812887 | -0.0339139 | 0.6832401 | 0.6417000 | -0.7948261 |
| wind_speed_mph | -0.1565951 | -0.0418597 | 0.0184732 | -0.2937780 | -0.0047797 | 0.0981261 | 0.6883297 | 0.2347295 | -0.3376340 | 0.7265675 | -0.0522510 | 0.7765453 | 0.6496602 | 0.3240315 | 0.0085482 | -0.9224867 | -0.9622048 | -0.9599856 | -0.6879652 | -0.7349847 | -0.7321703 | -0.7628128 | -0.7349849 | 0.0465964 | 0.1981271 | 0.2049762 | 0.9298068 | -0.7034344 | 1.0000000 | -0.0449803 | 0.1327197 | -0.7274398 | -0.7395832 | -0.3191683 | -0.5062268 | -0.5603983 | -0.4751151 | 0.1316506 |
| hourly_rain_inch_hr | -0.5853932 | -0.0704403 | -0.2176432 | -0.2338873 | 0.3771542 | 0.3699987 | 0.5008472 | -0.2152257 | -0.0847654 | 0.3710543 | -0.1615462 | 0.4930527 | 0.5881599 | 0.8403012 | -0.3481968 | -0.3089709 | -0.1385889 | -0.1514105 | 0.6514362 | 0.5453456 | 0.5514814 | 0.4467497 | 0.5453453 | -0.3878585 | 0.8781148 | 0.8393636 | -0.1048372 | 0.5071226 | -0.0449803 | 1.0000000 | -0.9760383 | -0.0162041 | -0.2635919 | 0.3903540 | -0.2583567 | 0.8325585 | 0.8792639 | -0.7269452 |
| wind_direction | 0.4160472 | 0.1274341 | 0.2995844 | 0.0701091 | -0.4621504 | -0.3248341 | -0.4131914 | 0.1706990 | -0.0095141 | -0.3893556 | 0.3429615 | -0.4596896 | -0.5947605 | -0.8389353 | 0.5201100 | 0.2502215 | 0.0893036 | 0.1017018 | -0.6198918 | -0.5073939 | -0.5136329 | -0.4033057 | -0.5073937 | 0.5641272 | -0.9183738 | -0.8942197 | 0.1126403 | -0.4507075 | 0.1327197 | -0.9760383 | 1.0000000 | -0.1849202 | 0.0884678 | -0.4162587 | 0.1097333 | -0.8951279 | -0.9350552 | 0.5833765 |
| days_btwn_clams_sort | 0.6221002 | -0.1820372 | -0.3004961 | 0.6775773 | 0.3127845 | -0.1796265 | -0.5694877 | 0.0439745 | 0.4636865 | -0.2266657 | -0.6107256 | -0.4200687 | -0.2241806 | -0.1019712 | -0.6125326 | 0.5779943 | 0.5702458 | 0.5701592 | 0.2131128 | 0.2058966 | 0.2053523 | 0.1940711 | 0.2058969 | -0.6455127 | 0.0965142 | 0.1420857 | -0.4251163 | 0.1271159 | -0.7274398 | -0.0162041 | -0.1849202 | 1.0000000 | 0.9038234 | 0.2427447 | 0.7140489 | 0.4920602 | 0.4348428 | 0.4046534 |
| clams_count | 0.6513410 | -0.1095267 | -0.1723985 | 0.6295397 | 0.1372108 | -0.2513523 | -0.6992874 | 0.0407731 | 0.4392980 | -0.4162456 | -0.4051013 | -0.6033655 | -0.4551523 | -0.3622227 | -0.3703103 | 0.7038381 | 0.6617840 | 0.6646049 | 0.1315758 | 0.1634997 | 0.1609126 | 0.1860633 | 0.1634999 | -0.3925326 | -0.1973273 | -0.1544615 | -0.4828256 | 0.1078947 | -0.7395832 | -0.2635919 | 0.0884678 | 0.9038234 | 1.0000000 | 0.2857309 | 0.5502340 | 0.2672788 | 0.1946438 | 0.4658585 |
| weight | -0.1435614 | -0.0272633 | -0.1060128 | 0.0286361 | 0.1638352 | 0.1041795 | -0.0219724 | -0.1393557 | 0.0833024 | -0.0598711 | -0.0857350 | -0.0383574 | 0.0425072 | 0.2295376 | -0.1723642 | 0.1555622 | 0.2300022 | 0.2245568 | 0.4374463 | 0.4087931 | 0.4103663 | 0.3777678 | 0.4087931 | -0.1992495 | 0.2885511 | 0.2748945 | -0.3051411 | 0.3812887 | -0.3191683 | 0.3903540 | -0.4162587 | 0.2427447 | 0.2857309 | 1.0000000 | -0.4501822 | 0.4907153 | 0.4829895 | -0.2848776 |
| avg_weight | 0.5857689 | -0.1115570 | -0.1592718 | 0.5385243 | 0.1285667 | -0.2188578 | -0.5285234 | 0.0839105 | 0.3501727 | -0.2535510 | -0.3928876 | -0.4208461 | -0.3052701 | -0.2803506 | -0.3481807 | 0.4875758 | 0.4400770 | 0.4431781 | -0.0086257 | 0.0125403 | 0.0106343 | 0.0285551 | 0.0125405 | -0.3618281 | -0.1487867 | -0.1069287 | -0.2770414 | -0.0339139 | -0.5062268 | -0.2583567 | 0.1097333 | 0.7140489 | 0.5502340 | -0.4501822 | 1.0000000 | 0.1441378 | 0.0919010 | 0.4661017 |
| clams_growth | -0.2647005 | -0.0922296 | -0.2645419 | 0.0843004 | 0.3943756 | 0.2241136 | 0.0331869 | -0.2429381 | 0.1647727 | 0.0045864 | -0.2771411 | 0.0371382 | 0.2091450 | 0.5564891 | -0.4511572 | 0.2042601 | 0.3553993 | 0.3441033 | 0.8195035 | 0.7459491 | 0.7499172 | 0.6709850 | 0.7459490 | -0.5050765 | 0.6817656 | 0.6597090 | -0.5063542 | 0.6832401 | -0.5603983 | 0.8325585 | -0.8951279 | 0.4920602 | 0.2672788 | 0.4907153 | 0.1441378 | 1.0000000 | 0.9950267 | -0.5339819 |
| growth_pct | -0.3005932 | -0.1028283 | -0.2785850 | 0.0537708 | 0.4181650 | 0.2500432 | 0.1181575 | -0.2300955 | 0.1342378 | 0.0920091 | -0.3001309 | 0.1327635 | 0.3001589 | 0.6301548 | -0.4777556 | 0.1061208 | 0.2619690 | 0.2502320 | 0.7882526 | 0.7044802 | 0.7090337 | 0.6215102 | 0.7044801 | -0.5304635 | 0.7480121 | 0.7253510 | -0.4263817 | 0.6417000 | -0.4751151 | 0.8792639 | -0.9350552 | 0.4348428 | 0.1946438 | 0.4829895 | 0.0919010 | 0.9950267 | 1.0000000 | -0.5520233 |
| clams_sr | 0.8831186 | -0.1677426 | -0.1364766 | 0.5883696 | 0.0524740 | -0.3468351 | -0.3359967 | 0.3819963 | 0.2394138 | 0.1233148 | -0.5405267 | -0.1097456 | -0.0838449 | -0.4005846 | -0.3664809 | 0.0223096 | -0.1328993 | -0.1225183 | -0.7939462 | -0.7683491 | -0.7707062 | -0.7365729 | -0.7683489 | -0.3411648 | -0.3326922 | -0.2556958 | 0.4143299 | -0.7948261 | 0.1316506 | -0.7269452 | 0.5833765 | 0.4046534 | 0.4658585 | -0.2848776 | 0.4661017 | -0.5339819 | -0.5520233 | 1.0000000 |

| interp_weight | interp_growth_pct | interp_oyster_chlorophyll | interp_water_temperature | interp_dissolved_oxygen | interp_conductivity | interp_visibility | interp_chlorophyll_a | interp_phosphate | interp_silicate | interp_nitrate_nitrite | interp_ammonia | interp_heterotrophic_bacteria | interp_large_phytoplankton | interp_synechococcus_population_1 | interp_synechococcus_population_2 | interp_prochlorococcus | interp_lysbeths_mystery_cells_events | ksf_rdo_concentration | ksf_rdo_saturation | ksf_oxygen_partial_pressure | ksf_actual_conductivity | ksf_specific_conductivity | ksf_salinity | ksf_density | ksf_total_dissolved_solids | ksf_chlorophyll_a_fluorescence | ksf_ammonium | ksf_ammonium_m_v | ksf_barometric_pressure | outdoor_temperature | wind_speed_mph | hourly_rain_inch_hr | wind_direction | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| interp_weight | 1.0000000 | 0.3234759 | -0.0913510 | 0.3490314 | -0.1265842 | -0.1951373 | 0.3687854 | 0.2105722 | -0.0843213 | -0.2150841 | 0.0668848 | 0.2282537 | 0.0073303 | -0.4052973 | -0.1027838 | 0.0007419 | 0.0235728 | -0.4004526 | 0.1811176 | 0.1737464 | 0.1737242 | 0.0231044 | 0.0061099 | 0.0064968 | -0.0098820 | 0.0061100 | -0.4153832 | 0.1271760 | 0.1564187 | -0.0948252 | -0.0392957 | -0.2725350 | 0.0118631 | -0.1226309 |
| interp_growth_pct | 0.3234759 | 1.0000000 | -0.5388897 | -0.1528308 | 0.0659844 | 0.0347963 | -0.0218784 | -0.0226320 | 0.0397986 | -0.2294761 | -0.2017876 | 0.0878710 | -0.3671222 | 0.1740144 | -0.3297297 | -0.2829287 | -0.0593737 | 0.1050958 | 0.4131824 | 0.4680781 | 0.4646410 | 0.5027389 | 0.5197118 | 0.5189250 | 0.5262616 | 0.5197118 | 0.0824236 | -0.0227650 | -0.0455588 | -0.5229801 | 0.5139035 | -0.4794204 | 0.1886814 | -0.1895607 |
| interp_oyster_chlorophyll | -0.0913510 | -0.5388897 | 1.0000000 | 0.3160325 | -0.1141143 | -0.0477048 | 0.0556124 | 0.0147480 | -0.0973312 | 0.3791780 | 0.3787317 | -0.1525900 | 0.6363475 | -0.3026164 | 0.5604919 | 0.4695293 | 0.0489962 | -0.1654460 | -0.7236923 | -0.8344526 | -0.8273690 | -0.9515436 | -0.9748177 | -0.9738230 | -0.9798152 | -0.9748178 | -0.1218234 | -0.0196287 | 0.0241502 | 0.9502717 | -0.9617032 | 0.8675895 | -0.4093271 | 0.4092570 |
| interp_water_temperature | 0.3490314 | -0.1528308 | 0.3160325 | 1.0000000 | -0.0657204 | -0.2650281 | 0.6482057 | 0.1101017 | -0.3109539 | -0.4805062 | 0.1818541 | 0.2940774 | -0.0599312 | -0.5934387 | -0.2441516 | -0.1728060 | -0.3813279 | -0.4479796 | 0.2397450 | 0.1168555 | 0.1249848 | -0.5234111 | -0.4996062 | -0.5019132 | -0.4737839 | -0.4996059 | -0.4232381 | -0.2669770 | -0.1954463 | 0.1456517 | -0.5416504 | -0.1565951 | -0.5853932 | 0.4160472 |
| interp_dissolved_oxygen | -0.1265842 | 0.0659844 | -0.1141143 | -0.0657204 | 1.0000000 | 0.2319534 | -0.2129630 | -0.2818786 | -0.1636619 | -0.1108632 | -0.2637840 | -0.3180597 | -0.2899848 | 0.2351851 | -0.1355177 | -0.2045156 | -0.2169682 | 0.2781064 | 0.1148541 | 0.1173851 | 0.1177972 | 0.1023190 | 0.1336775 | 0.1322711 | 0.1591253 | 0.1336775 | 0.3167944 | -0.2036098 | -0.2235560 | -0.1488079 | 0.1616038 | -0.0418597 | -0.0704403 | 0.1274341 |
| interp_conductivity | -0.1951373 | 0.0347963 | -0.0477048 | -0.2650281 | 0.2319534 | 1.0000000 | -0.3550157 | -0.5998299 | -0.0795675 | -0.1452676 | 0.1190473 | -0.2152160 | -0.2226307 | 0.3888135 | -0.2263441 | -0.2632717 | -0.4589120 | 0.4920133 | 0.1291550 | 0.1098263 | 0.1120566 | 0.0157606 | 0.0680932 | 0.0656345 | 0.1127614 | 0.0680932 | 0.4853660 | -0.3696082 | -0.3920589 | -0.1268462 | 0.1132979 | 0.0184732 | -0.2176432 | 0.2995844 |
| interp_visibility | 0.3687854 | -0.0218784 | 0.0556124 | 0.6482057 | -0.2129630 | -0.3550157 | 1.0000000 | 0.5522508 | -0.0947801 | -0.2502225 | 0.2410925 | 0.4725783 | 0.0768136 | -0.5494701 | -0.1840184 | -0.0919120 | -0.1334711 | -0.5326582 | 0.2514661 | 0.1940241 | 0.1973939 | -0.1993019 | -0.2037581 | -0.2043899 | -0.2065081 | -0.2037579 | -0.5262559 | -0.0038245 | 0.0506186 | -0.0182157 | -0.2594965 | -0.2937780 | -0.2338873 | 0.0701091 |
| interp_chlorophyll_a | 0.2105722 | -0.0226320 | 0.0147480 | 0.1101017 | -0.2818786 | -0.5998299 | 0.5522508 | 1.0000000 | 0.2856368 | 0.3588692 | 0.0276973 | 0.2930864 | 0.4432634 | -0.4542994 | 0.3410615 | 0.3920397 | 0.4771970 | -0.5799557 | -0.2051033 | -0.1610904 | -0.1652734 | 0.0578687 | -0.0154719 | -0.0118535 | -0.0789170 | -0.0154719 | -0.5871394 | 0.5329029 | 0.5524064 | 0.1454145 | -0.0698531 | -0.0047797 | 0.3771542 | -0.4621504 |
| interp_phosphate | -0.0843213 | 0.0397986 | -0.0973312 | -0.3109539 | -0.1636619 | -0.0795675 | -0.0947801 | 0.2856368 | 1.0000000 | 0.5660911 | 0.1390703 | 0.4437642 | 0.2403967 | 0.0704494 | 0.1868674 | 0.2298244 | 0.3269728 | -0.0371624 | -0.2008496 | -0.1360496 | -0.1407781 | 0.2145857 | 0.1783425 | 0.1806208 | 0.1452450 | 0.1783423 | -0.0191587 | 0.2977059 | 0.2746158 | 0.0199737 | 0.1786904 | 0.0981261 | 0.3699987 | -0.3248341 |
| interp_silicate | -0.2150841 | -0.2294761 | 0.3791780 | -0.4805062 | -0.1108632 | -0.1452676 | -0.2502225 | 0.3588692 | 0.5660911 | 1.0000000 | 0.1222968 | -0.0004807 | 0.7401299 | -0.0510525 | 0.7769636 | 0.7358405 | 0.6781906 | -0.1454307 | -0.8116844 | -0.7465947 | -0.7519289 | -0.1194767 | -0.2052439 | -0.2001345 | -0.2742370 | -0.2052442 | -0.1160208 | 0.5831631 | 0.5629555 | 0.5838129 | -0.1978858 | 0.6883297 | 0.5008472 | -0.4131914 |
| interp_nitrate_nitrite | 0.0668848 | -0.2017876 | 0.3787317 | 0.1818541 | -0.2637840 | 0.1190473 | 0.2410925 | 0.0276973 | 0.1390703 | 0.1222968 | 1.0000000 | 0.3335486 | 0.4218813 | -0.2415770 | -0.0082736 | 0.1348142 | -0.0378669 | -0.2408958 | -0.1928328 | -0.2491312 | -0.2456062 | -0.4010053 | -0.4102686 | -0.4101014 | -0.4124758 | -0.4102686 | -0.1854731 | -0.0112523 | 0.0195065 | 0.3396210 | -0.4204995 | 0.2347295 | -0.2152257 | 0.1706990 |
| interp_ammonia | 0.2282537 | 0.0878710 | -0.1525900 | 0.2940774 | -0.3180597 | -0.2152160 | 0.4725783 | 0.2930864 | 0.4437642 | -0.0004807 | 0.3335486 | 1.0000000 | -0.0381747 | -0.2510432 | -0.3243820 | -0.1985097 | -0.0641393 | -0.2892883 | 0.2963946 | 0.2800056 | 0.2809420 | 0.0532133 | 0.0586622 | 0.0579085 | 0.0612364 | 0.0586623 | -0.2640421 | -0.0264327 | -0.0027462 | -0.1946828 | 0.0254673 | -0.3376340 | -0.0847654 | -0.0095141 |
| interp_heterotrophic_bacteria | 0.0073303 | -0.3671222 | 0.6363475 | -0.0599312 | -0.2899848 | -0.2226307 | 0.0768136 | 0.4432634 | 0.2403967 | 0.7401299 | 0.4218813 | -0.0381747 | 1.0000000 | -0.5360501 | 0.8789411 | 0.9205836 | 0.6812707 | -0.5629514 | -0.8877300 | -0.8678669 | -0.8707803 | -0.4252525 | -0.5352751 | -0.5294539 | -0.6212698 | -0.5352753 | -0.5551741 | 0.6979044 | 0.7207483 | 0.8204889 | -0.5668166 | 0.7265675 | 0.3710543 | -0.3893556 |
| interp_large_phytoplankton | -0.4052973 | 0.1740144 | -0.3026164 | -0.5934387 | 0.2351851 | 0.3888135 | -0.5494701 | -0.4542994 | 0.0704494 | -0.0510525 | -0.2415770 | -0.2510432 | -0.5360501 | 1.0000000 | -0.3648509 | -0.5254498 | -0.3812589 | 0.9552025 | 0.2603205 | 0.2744921 | 0.2751979 | 0.2942632 | 0.3800012 | 0.3762880 | 0.4496783 | 0.3800012 | 0.9449553 | -0.5543873 | -0.6166687 | -0.3861873 | 0.4652032 | -0.0522510 | -0.1615462 | 0.3429615 |
| interp_synechococcus_population_1 | -0.1027838 | -0.3297297 | 0.5604919 | -0.2441516 | -0.1355177 | -0.2263441 | -0.1840184 | 0.3410615 | 0.1868674 | 0.7769636 | -0.0082736 | -0.3243820 | 0.8789411 | -0.3648509 | 1.0000000 | 0.9436791 | 0.7480325 | -0.3837403 | -0.9419402 | -0.8939279 | -0.8986056 | -0.2995903 | -0.4103768 | -0.4042186 | -0.4982813 | -0.4103771 | -0.3976795 | 0.7266781 | 0.7293942 | 0.7791376 | -0.4263505 | 0.7765453 | 0.4930527 | -0.4596896 |
| interp_synechococcus_population_2 | 0.0007419 | -0.2829287 | 0.4695293 | -0.1728060 | -0.2045156 | -0.2632717 | -0.0919120 | 0.3920397 | 0.2298244 | 0.7358405 | 0.1348142 | -0.1985097 | 0.9205836 | -0.5254498 | 0.9436791 | 1.0000000 | 0.8293406 | -0.5742000 | -0.8816949 | -0.8200803 | -0.8259709 | -0.2159316 | -0.3412148 | -0.3344355 | -0.4430526 | -0.3412150 | -0.5834799 | 0.8464878 | 0.8561089 | 0.7127158 | -0.3777729 | 0.6496602 | 0.5881599 | -0.5947605 |
| interp_prochlorococcus | 0.0235728 | -0.0593737 | 0.0489962 | -0.3813279 | -0.2169682 | -0.4589120 | -0.1334711 | 0.4771970 | 0.3269728 | 0.6781906 | -0.0378669 | -0.0641393 | 0.6812707 | -0.3812589 | 0.7480325 | 0.8293406 | 1.0000000 | -0.5429470 | -0.6404636 | -0.5195533 | -0.5293928 | 0.2122566 | 0.0868102 | 0.0937653 | -0.0215978 | 0.0868100 | -0.5517825 | 0.9305750 | 0.9181134 | 0.3403672 | 0.0438956 | 0.3240315 | 0.8403012 | -0.8389353 |
| interp_lysbeths_mystery_cells_events | -0.4004526 | 0.1050958 | -0.1654460 | -0.4479796 | 0.2781064 | 0.4920133 | -0.5326582 | -0.5799557 | -0.0371624 | -0.1454307 | -0.2408958 | -0.2892883 | -0.5629514 | 0.9552025 | -0.3837403 | -0.5742000 | -0.5429470 | 1.0000000 | 0.2609594 | 0.2396224 | 0.2428721 | 0.1197646 | 0.2192529 | 0.2146858 | 0.3030029 | 0.2192529 | 0.9739163 | -0.6855490 | -0.7368587 | -0.3003455 | 0.3090457 | 0.0085482 | -0.3481968 | 0.5201100 |
| ksf_rdo_concentration | 0.1811176 | 0.4131824 | -0.7236923 | 0.2397450 | 0.1148541 | 0.1291550 | 0.2514661 | -0.2051033 | -0.2008496 | -0.8116844 | -0.1928328 | 0.2963946 | -0.8877300 | 0.2603205 | -0.9419402 | -0.8816949 | -0.6404636 | 0.2609594 | 1.0000000 | 0.9841108 | 0.9863184 | 0.4772012 | 0.5695713 | 0.5643500 | 0.6390054 | 0.5695716 | 0.2408733 | -0.5611369 | -0.5654086 | -0.8940100 | 0.5669547 | -0.9224867 | -0.3089709 | 0.2502215 |
| ksf_rdo_saturation | 0.1737464 | 0.4680781 | -0.8344526 | 0.1168555 | 0.1173851 | 0.1098263 | 0.1940241 | -0.1610904 | -0.1360496 | -0.7465947 | -0.2491312 | 0.2800056 | -0.8678669 | 0.2744921 | -0.8939279 | -0.8200803 | -0.5195533 | 0.2396224 | 0.9841108 | 1.0000000 | 0.9999156 | 0.6249132 | 0.7037013 | 0.6993164 | 0.7596117 | 0.7037015 | 0.2120478 | -0.4363655 | -0.4503518 | -0.9566189 | 0.6971053 | -0.9622048 | -0.1385889 | 0.0893036 |
| ksf_oxygen_partial_pressure | 0.1737242 | 0.4646410 | -0.8273690 | 0.1249848 | 0.1177972 | 0.1120566 | 0.1973939 | -0.1652734 | -0.1407781 | -0.7519289 | -0.2456062 | 0.2809420 | -0.8707803 | 0.2751979 | -0.8986056 | -0.8259709 | -0.5293928 | 0.2428721 | 0.9863184 | 0.9999156 | 1.0000000 | 0.6149809 | 0.6949539 | 0.6904989 | 0.7520040 | 0.6949541 | 0.2158518 | -0.4466848 | -0.4600720 | -0.9532733 | 0.6887858 | -0.9599856 | -0.1514105 | 0.1017018 |
| ksf_actual_conductivity | 0.0231044 | 0.5027389 | -0.9515436 | -0.5234111 | 0.1023190 | 0.0157606 | -0.1993019 | 0.0578687 | 0.2145857 | -0.1194767 | -0.4010053 | 0.0532133 | -0.4252525 | 0.2942632 | -0.2995903 | -0.2159316 | 0.2122566 | 0.1197646 | 0.4772012 | 0.6249132 | 0.6149809 | 1.0000000 | 0.9906982 | 0.9916629 | 0.9680363 | 0.9906981 | 0.0739218 | 0.2593693 | 0.2040619 | -0.8149692 | 0.9777205 | -0.6879652 | 0.6514362 | -0.6198918 |
| ksf_specific_conductivity | 0.0061099 | 0.5197118 | -0.9748177 | -0.4996062 | 0.1336775 | 0.0680932 | -0.2037581 | -0.0154719 | 0.1783425 | -0.2052439 | -0.4102686 | 0.0586622 | -0.5352751 | 0.3800012 | -0.4103768 | -0.3412148 | 0.0868102 | 0.2192529 | 0.5695713 | 0.7037013 | 0.6949539 | 0.9906982 | 1.0000000 | 0.9999729 | 0.9931595 | 1.0000000 | 0.1764503 | 0.1255371 | 0.0692212 | -0.8766105 | 0.9952781 | -0.7349847 | 0.5453456 | -0.5073939 |
| ksf_salinity | 0.0064968 | 0.5189250 | -0.9738230 | -0.5019132 | 0.1322711 | 0.0656345 | -0.2043899 | -0.0118535 | 0.1806208 | -0.2001345 | -0.4101014 | 0.0579085 | -0.5294539 | 0.3762880 | -0.4042186 | -0.3344355 | 0.0937653 | 0.2146858 | 0.5643500 | 0.6993164 | 0.6904989 | 0.9916629 | 0.9999729 | 1.0000000 | 0.9922847 | 0.9999729 | 0.1717245 | 0.1327587 | 0.0764089 | -0.8734302 | 0.9948862 | -0.7321703 | 0.5514814 | -0.5136329 |
| ksf_density | -0.0098820 | 0.5262616 | -0.9798152 | -0.4737839 | 0.1591253 | 0.1127614 | -0.2065081 | -0.0789170 | 0.1452450 | -0.2742370 | -0.4124758 | 0.0612364 | -0.6212698 | 0.4496783 | -0.4982813 | -0.4430526 | -0.0215978 | 0.3030029 | 0.6390054 | 0.7596117 | 0.7520040 | 0.9680363 | 0.9931595 | 0.9922847 | 1.0000000 | 0.9931596 | 0.2635036 | 0.0088565 | -0.0476442 | -0.9157638 | 0.9957898 | -0.7628128 | 0.4467497 | -0.4033057 |
| ksf_total_dissolved_solids | 0.0061100 | 0.5197118 | -0.9748178 | -0.4996059 | 0.1336775 | 0.0680932 | -0.2037579 | -0.0154719 | 0.1783423 | -0.2052442 | -0.4102686 | 0.0586623 | -0.5352753 | 0.3800012 | -0.4103771 | -0.3412150 | 0.0868100 | 0.2192529 | 0.5695716 | 0.7037015 | 0.6949541 | 0.9906981 | 1.0000000 | 0.9999729 | 0.9931596 | 1.0000000 | 0.1764503 | 0.1255369 | 0.0692210 | -0.8766106 | 0.9952781 | -0.7349849 | 0.5453453 | -0.5073937 |
| ksf_chlorophyll_a_fluorescence | -0.4153832 | 0.0824236 | -0.1218234 | -0.4232381 | 0.3167944 | 0.4853660 | -0.5262559 | -0.5871394 | -0.0191587 | -0.1160208 | -0.1854731 | -0.2640421 | -0.5551741 | 0.9449553 | -0.3976795 | -0.5834799 | -0.5517825 | 0.9739163 | 0.2408733 | 0.2120478 | 0.2158518 | 0.0739218 | 0.1764503 | 0.1717245 | 0.2635036 | 0.1764503 | 1.0000000 | -0.7160863 | -0.7664177 | -0.2663666 | 0.2697243 | 0.0465964 | -0.3878585 | 0.5641272 |
| ksf_ammonium | 0.1271760 | -0.0227650 | -0.0196287 | -0.2669770 | -0.2036098 | -0.3696082 | -0.0038245 | 0.5329029 | 0.2977059 | 0.5831631 | -0.0112523 | -0.0264327 | 0.6979044 | -0.5543873 | 0.7266781 | 0.8464878 | 0.9305750 | -0.6855490 | -0.5611369 | -0.4363655 | -0.4466848 | 0.2593693 | 0.1255371 | 0.1327587 | 0.0088565 | 0.1255369 | -0.7160863 | 1.0000000 | 0.9967078 | 0.2787391 | 0.0641183 | 0.1981271 | 0.8781148 | -0.9183738 |
| ksf_ammonium_m_v | 0.1564187 | -0.0455588 | 0.0241502 | -0.1954463 | -0.2235560 | -0.3920589 | 0.0506186 | 0.5524064 | 0.2746158 | 0.5629555 | 0.0195065 | -0.0027462 | 0.7207483 | -0.6166687 | 0.7293942 | 0.8561089 | 0.9181134 | -0.7368587 | -0.5654086 | -0.4503518 | -0.4600720 | 0.2040619 | 0.0692212 | 0.0764089 | -0.0476442 | 0.0692210 | -0.7664177 | 0.9967078 | 1.0000000 | 0.3133194 | 0.0034481 | 0.2049762 | 0.8393636 | -0.8942197 |
| ksf_barometric_pressure | -0.0948252 | -0.5229801 | 0.9502717 | 0.1456517 | -0.1488079 | -0.1268462 | -0.0182157 | 0.1454145 | 0.0199737 | 0.5838129 | 0.3396210 | -0.1946828 | 0.8204889 | -0.3861873 | 0.7791376 | 0.7127158 | 0.3403672 | -0.3003455 | -0.8940100 | -0.9566189 | -0.9532733 | -0.8149692 | -0.8766105 | -0.8734302 | -0.9157638 | -0.8766106 | -0.2663666 | 0.2787391 | 0.3133194 | 1.0000000 | -0.8754851 | 0.9298068 | -0.1048372 | 0.1126403 |
| outdoor_temperature | -0.0392957 | 0.5139035 | -0.9617032 | -0.5416504 | 0.1616038 | 0.1132979 | -0.2594965 | -0.0698531 | 0.1786904 | -0.1978858 | -0.4204995 | 0.0254673 | -0.5668166 | 0.4652032 | -0.4263505 | -0.3777729 | 0.0438956 | 0.3090457 | 0.5669547 | 0.6971053 | 0.6887858 | 0.9777205 | 0.9952781 | 0.9948862 | 0.9957898 | 0.9952781 | 0.2697243 | 0.0641183 | 0.0034481 | -0.8754851 | 1.0000000 | -0.7034344 | 0.5071226 | -0.4507075 |
| wind_speed_mph | -0.2725350 | -0.4794204 | 0.8675895 | -0.1565951 | -0.0418597 | 0.0184732 | -0.2937780 | -0.0047797 | 0.0981261 | 0.6883297 | 0.2347295 | -0.3376340 | 0.7265675 | -0.0522510 | 0.7765453 | 0.6496602 | 0.3240315 | 0.0085482 | -0.9224867 | -0.9622048 | -0.9599856 | -0.6879652 | -0.7349847 | -0.7321703 | -0.7628128 | -0.7349849 | 0.0465964 | 0.1981271 | 0.2049762 | 0.9298068 | -0.7034344 | 1.0000000 | -0.0449803 | 0.1327197 |
| hourly_rain_inch_hr | 0.0118631 | 0.1886814 | -0.4093271 | -0.5853932 | -0.0704403 | -0.2176432 | -0.2338873 | 0.3771542 | 0.3699987 | 0.5008472 | -0.2152257 | -0.0847654 | 0.3710543 | -0.1615462 | 0.4930527 | 0.5881599 | 0.8403012 | -0.3481968 | -0.3089709 | -0.1385889 | -0.1514105 | 0.6514362 | 0.5453456 | 0.5514814 | 0.4467497 | 0.5453453 | -0.3878585 | 0.8781148 | 0.8393636 | -0.1048372 | 0.5071226 | -0.0449803 | 1.0000000 | -0.9760383 |
| wind_direction | -0.1226309 | -0.1895607 | 0.4092570 | 0.4160472 | 0.1274341 | 0.2995844 | 0.0701091 | -0.4621504 | -0.3248341 | -0.4131914 | 0.1706990 | -0.0095141 | -0.3893556 | 0.3429615 | -0.4596896 | -0.5947605 | -0.8389353 | 0.5201100 | 0.2502215 | 0.0893036 | 0.1017018 | -0.6198918 | -0.5073939 | -0.5136329 | -0.4033057 | -0.5073937 | 0.5641272 | -0.9183738 | -0.8942197 | 0.1126403 | -0.4507075 | 0.1327197 | -0.9760383 | 1.0000000 |
